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Fingerprint enhancement by shape adaptation of scale-space operators with automatic scale selection
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0002-9081-2170
2000 (English)In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 9, no 12, 2027-2042 p.Article in journal (Refereed) Published
Abstract [en]

This work presents two mechanisms for processing fingerprint images; shape-adapted smoothing based on second moment descriptors and automatic scale selection based on normalized derivatives. The shape adaptation procedure adapts the smoothing operation to the local ridge structures, which allows interrupted ridges to be joined without destroying essential singularities such as branching points and enforces continuity of their directional fields. The Scale selection procedure estimates local ridge width and adapts the amount of smoothing to the local amount of noise. In addition, a ridgeness measure is defined, which reflects how well the local image structure agrees with a qualitative ridge model, and is used for spreading the results of shape adaptation into noisy areas. The combined approach makes it possible to resolve fine scale structures in clear areas while reducing the risk of enhancing noise in blurred or fragmented areas. The result is a reliable and adaptively detailed estimate of the ridge orientation field and ridge width, as well as a Smoothed grey-level version of the input image. We propose that these general techniques should be of interest to developers of automatic fingerprint identification systems as well as in other applications of processing related types of imagery.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2000. Vol. 9, no 12, 2027-2042 p.
Keyword [en]
affine, automatic scale selection, diffusion, enhancement, fingerprint, image processing, scale-space, edge-detection, surface orientation, ridge detection, texture, algorithm, system, images, cues
National Category
Computer Science Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:kth:diva-20181DOI: 10.1109/83.887971ISI: 000165517200004OAI: diva2:338874

QC 20100525

Available from: 2013-04-19 Created: 2010-08-10 Last updated: 2013-04-19Bibliographically approved

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